Advertisement

Applied Geomatics

, Volume 6, Issue 4, pp 229–244 | Cite as

Urban structure in Kolkata: metrics and modelling through geo-informatics

  • T. V. RamachandraEmail author
  • Bharath H. Aithal
  • M. V. Sowmyashree
Original Paper

Abstract

Urbanization connotes to the growth of a metropolis on being subjected to criteria such as economic, social and political forces as well as the geomorphology of the metropolis. As population and its activities increase in a city, the boundary of the city expands to accommodate growth along the urban fringes, leading to fragmented urban morphology, thereby impacting local ecology. Towns and cities had bloomed post-independence in India, causing changes in the land use along the myriad landscapes and ecosystems of the country. These urban ecosystems were a consequence of unplanned development of industrial centres and uncontrolled growth of residential colonies, which altogether became hubs for economic, social, cultural, and political activities. A visualization of the past trends and patterns of growth enable the planning machineries to plan for appropriate basic infrastructure facilities (water, electricity, sanitation, etc.). This communication analyses the spatial patterns of Kolkata municipality—the 13th most populous and 8th largest urban agglomeration in the world. It has been one of the most prominent urban areas in eastern India which was once considered the capital of India during the erstwhile British colonial rule. The spatial patterns of urbanization of Kolkata with 10 km buffer have been analysed using temporal remote sensing data with zonal gradients and spatial metrics. The study area was divided into four zones and each zone was further divided into concentric circles of 1 km incrementing radii to understand the patterns and extent of urbanization at local levels. Its land use analysis has revealed a decline of vegetation from 33.6 % (1980) to 7.36 % (2010). During 2010, Kolkata’s built-up had constituted 8.6 %, water bodies comprised of 3.15 %, whereas other categories made up about 80.87 %. Increased Shannon’s entropy during the last decade highlights the tendency of sprawl that necessitated policy interventions to provide basic amenities. Spatial patterns through metrics indicated a compact and simple structured growth at the centre of the city and a distributed complex shape in the buffer region. Further, these metrics indicated that the city is on the verge of becoming a single large urban patch that would affect its ecological integrity. Temporal analyses of spatial patterns of urbanization help the city administration and city planners to visualize and understand the growth of the city so that they can provide better resource planning to create a sustainable city.

Keywords

Urbanization Urban sprawl Urban pattern Remote sensing Spatial metrics Kolkata 

Notes

Acknowledgments

We are grateful to NRDMS Division, The Ministry of Science and Technology, Government of India; ISRO-IISc Space Technology Cell, Indian Institute of Science; and Centre for infrastructure, Sustainable Transportation and Urban Planning (CiSTUP), Indian Institute of Science for the financial and infrastructure support. Remote sensing data were downloaded from public domain (http://glcf.umiacs.umd.edu/data). Latest data of IRS 1D were procured from National Remote Sensing Centre, Hyderabad

References

  1. Angel S, Parent J, Civco D (2007) Urban sprawl metrics: an analysis of global urban expansion using GIS. Proceedings of ASPRS 2007 Annual Conference, Tampa, Florida May 7–11.URL: http://clear.uconn.edu/publications/research/tech_papers/Angel_et_al_ASPRS2007.pdf
  2. Bhatta B (2009) Analysis of urban growth pattern using remote sensing and GIS: a case study of Kolkata, India. Int J Remote Sens 30(18):4733–4746CrossRefGoogle Scholar
  3. Bhatta B (2010) Analysis of urban growth and sprawl from remote sensing data. Springer, BerlinCrossRefGoogle Scholar
  4. Bonnerjee S (2012) Multi-functional role of the urban waterways: a case study of Kolkata. In Proceedings of Internal River Symposium, at Melbourne, Australia, 8–12 Oct. 2012Google Scholar
  5. Bunting SW, Kundu N, Mukherjee M (2002) Situation analysis of production systems and natural resource use in PU Kolkata. Institute of Aquaculture, SterlingGoogle Scholar
  6. Census of India, 2011, (http://censusindia.gov.in/)
  7. Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37:35–46CrossRefGoogle Scholar
  8. Congalton RG, Oderwald RG, Mead RA (1983) Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogramm Eng Remote Sens 49:1671–1678Google Scholar
  9. Duda RO, Hart PE, Stork DG (2000) Pattern classification. 2nd edn. Wiley, New York, ISBN 9814-12-602-0Google Scholar
  10. Galster G, Hanson R, Ratclfee MR (2001) Wrestling sprawl to the ground: defining and measuring an elusive concept. Hous Policy Debate 12(4):681–717CrossRefGoogle Scholar
  11. Gregory P (2005) A synthesis of peri- urban research of Kumasi, Hubli Dharwad and Kolkata peri-urban interfaces, Development Planning Unit (DPU), University college London. Available at www.nrsp.org/database/documents/2812.pdf
  12. Gustafson EJ (1998) Quantifying landscape spatial pattern: what is the state of the art? Ecosystems 1:143–156CrossRefGoogle Scholar
  13. Herold M, Clarke K, Scepan J (2002) The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environ Plan 34:1443–1458CrossRefGoogle Scholar
  14. Herold M, Goldstein N, Clarke K (2003) The spatiotemporal form of urban growth: measurement, analysis and modeling. Remote Sens Environ 86:286–302CrossRefGoogle Scholar
  15. Jiang F, Liu S, Yuan H, Zhang Q (2007) Measuring urban sprawl in Beijing with geo-spatial indices. J Geogr Sci 17:469–478CrossRefGoogle Scholar
  16. Lillesand TM, Kiefer RW (2002) Remote sensing and image interpretation, 4th edition, Wiley, New York, ISBN 9971-51-427-3. 215–216Google Scholar
  17. Maiti S, Agrawal PK (2005) Environmental degradation in the context of growing urbanization: a focus on the metropolitan cities of India. J Hum Ecol 17(4):277–287Google Scholar
  18. Maktav D, Erbek FS (2005) Analyse of urban growth using multi-temporal satellite data in Istanbul, Turkey. Int J Remote Sens 26(4):797–810CrossRefGoogle Scholar
  19. McGarigal K, Marks BJ (1995) FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. USDA Forest Service General Technical Report PNW-351Google Scholar
  20. Mitrakis NE, Topalogou CA et al (2008) A novel self-organising neuro-fuzzy multilayered classifier for land cover classification of a VHR image. Int J Remote Sens 29:4061–4087CrossRefGoogle Scholar
  21. Peiser R (2001) Decomposing urban sprawl. Town Plan Rev 72(3):275–298Google Scholar
  22. Rahman A (2007) Application of remote sensing and GIS techniques for urban environmental management and sustainable development of Delhi, India, applied remote sensing for urban planning, governance and sustainability. Springer, New York, pp 165–197CrossRefGoogle Scholar
  23. Ramachandra TV, Kumar U (2008) Wetlands of greater Bangalore, India: automatic delineation through pattern classifiers. Electronic Green J 26:1076–7975Google Scholar
  24. Ramachandra TV, Bharath AH, Durgappa DS (2012) Insights to urban dynamics through landscape spatial pattern analysis. Int J Appl Earth Obs Geoinformation 18:329–343CrossRefGoogle Scholar
  25. Stow DA, Chen DM (2002) Sensitivity of multi-temporal NOAA AVHRR data of an urbanizing region to land use/cover changes and mis-registration. Remote Sens Environ 80:297–307CrossRefGoogle Scholar
  26. Sudhira HS, Ramachandra TV, Jagadish KS (2004) Urban sprawl: metrics, dynamics and modelling using G1S. Int J Appl Earth Obs Geoinformation 5:29–39CrossRefGoogle Scholar
  27. Sudhira HS, Ramachandra TV, Bala Subramanya MH (2007) City profile: Bangalore. Cities l24 (4):379–390Google Scholar
  28. Taubenbock H, Esch T, Thiel M et al (2008a) Urban structure analysis of mega city Mexico City using multi-sensoral remote sensing data. In Proceedings of SPIE-Europe (international society for optical engineering) conference, Cardiff, WalesGoogle Scholar
  29. Taubenbock H, Wegmann M, Berger C, Breunig M, Roth A, Mehl H, (2008b) Spatiotemporal analysis of Indian megacities. In Proceedings of the international archives of the photogrammetry, remote sensing and spatial information sciences, XXXVII: 75–82, Part B2Google Scholar
  30. Torrens PM, Alberti M (2000) Measuring sprawl. Working paper no. 27, Centre for Advanced Spatial Analysis. University College, LondonGoogle Scholar
  31. Turner BL (1994) Local faces, global flows: the role of land use and land cover in global environmental change. Land Degrad Dev 5:71–78CrossRefGoogle Scholar
  32. Turner MG, Gardner RH, O'Neill RV (2001) Landscape ecology in theory and practice: pattern and process. Springer, New YorkGoogle Scholar
  33. United Nations Population Fund (2007) The state of world population 2007: unleashing the potential of urban growth, United Nations publications, and chapter 1Google Scholar
  34. Yeh AGO, Li X (1999) Economic development and agricultural land loss in the Pearl River delta, China. Habitat Int 23:373–390CrossRefGoogle Scholar
  35. Zhang L, Wu J, Zhen Y, Shu J (2004) A GIS-based gradient analysis of urban landscape pattern of Shanghai metropolitan area, China. Landsc Urban Plan 69:1–16CrossRefGoogle Scholar

Copyright information

© Società Italiana di Fotogrammetria e Topografia (SIFET) 2014

Authors and Affiliations

  • T. V. Ramachandra
    • 1
    • 2
    • 3
    • 4
    Email author
  • Bharath H. Aithal
    • 1
    • 2
  • M. V. Sowmyashree
    • 1
  1. 1.Energy & Wetlands Research GroupCenter for Ecological Sciences [CES]BangaloreIndia
  2. 2.Centre for Sustainable Technologies (Astra)BangaloreIndia
  3. 3.Indian Institute of ScienceCentre for infrastructure, Sustainable Transportation and Urban Planning [CiSTUP]BangaloreIndia
  4. 4.Indian Institute of Science, Energy & Wetlands Research Group, CES TE15Centre for Ecological SciencesBangaloreIndia

Personalised recommendations